EEB BootCamp 2020: Difference between revisions

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<syntaxhighlight lang='bash'>
<syntaxhighlight lang='bash'>
ls -lrt # long list, in reverse timeline
ls -lrt # long list, in reverse timeline
ls cov_data # a folder of 100 CoV2 genomes in FASTA format, pairwise genome alignment sam and indexed sorted bam files generated by bwa (or nucmer) and samtools  
ls cov_data # a folder of 100 CoV2 genomes in FASTA format, pairwise genome alignment sam and indexed sorted bam files generated by bwa (or nucmer) and samtools  
# We skipped bwa (or nucmer) and samtools part of the tutorial for time constrain. The bash script used to generate these files is available on request  
# We skipped bwa (or nucmer) and samtools part of the tutorial for time constrain. The bash script used to generate these files is available on request  
ls cov_data/*sorted.bam | wc # 100 sorted.bam files correspond to 100 sequence files
ls cov_data/*sorted.bam | wc # 100 sorted.bam files correspond to 100 sequence files

Revision as of 03:43, 4 August 2020

Bioinformatics Boot Camp for Ecology & Evolution: Genomic Epidemiology
Thursday, Aug 6, 2020, 2 - 3:30pm
Instructors: Dr Weigang Qiu & Ms Saymon Akther
Email: weigang@genectr.hunter.cuny.edu
Lab Website: http://diverge.hunter.cuny.edu/labwiki/
CoV Genome Tracker Coronavirus evolutuon Lyme Disease (Borreliella)
Spike protein alignment
Gains & losses of host-defense genes among Lyme pathogen genomes (Qiu & Martin 2014)

Case studies

CoV genome data set

  • N=100 SARS-CoV-2 genomes collected during January, February & March 2020. Data source & acknowledgement GIDAID (Warning: You need to acknowledge GISAID if you reuse the data in any publication)
  • Download the folder "bootcamp_august_6th_2020": data file
  • unzip the folder
unizip bootcamp_august_6th_2020.zip
  • View files
ls -lrt # long list, in reverse timeline

ls cov_data # a folder of 100 CoV2 genomes in FASTA format, pairwise genome alignment sam and indexed sorted bam files generated by bwa (or nucmer) and samtools 

# We skipped bwa (or nucmer) and samtools part of the tutorial for time constrain. The bash script used to generate these files is available on request 
ls cov_data/*sorted.bam | wc # 100 sorted.bam files correspond to 100 sequence files
less ref.fas # NC_045512 as reference sequence, "q" to quit
less metadata_cov.txt # a tsv file that contains collection dates and geographic information of 100 CoV2 genomes
wc metadata_cov.txt
file TCS.jar # Java application
less bcf-snp-call.sh # a file contain all the bash commands required to call SNPs and generate vcf file of 100 CoV2 genomes
less ploidy.txt # to specify the ploidy=1 during vcf SNP call
less rgb.txt #rgb color code to color the phylogenetic network

Bioinformatics tools for genomic epidemiology

Tutorial

  • 2-2:30: Introduction on pathogen phylogenomics
  • 2:30-2:45: Demo: sequence manipulation with BpWrapper
bioseq --man
bioseq -i'genbank' ref.gb > ref.fas
bioseq -n Jan-Feb.mafft
bioaln --man
bioaln -n -i'fasta' Jan-Feb.mafft
bioaln -l -i'fasta' Jan-Feb.mafft
bioaln -n -i'phylip' cov-565strains-617snvs.phy
bioaln -l -i'phylip' cov-565strains-617snvs.phy
FastTree -nt cov-565strains-617snvs.phy > cov.dnd
biotree --man
biotree -n cov.dnd
biotree -l cov.dnd
  • 2:45-3:10: build haplotype network with TCS
# Data pre-processing
# 1. Download genomes & meta data from GISAID
# 2. Run dnadist against a reference genome
man nucmer
dnadiff -h
dnadiff ref.fas <query FASTA>
mkdir fasta-files
cd fasta-files
for f in *.fas; do dnadiff ref.fas $f; done
<to be added: plot in R seq diff vs collection date>
# 3. Remove mis-assembled and reverse-complemented genomes
bioseq -d'file:'
# 4. Remove genomes with more than 10 non-ATCG bases
bioseq -d'ambig:10'
# 5. Run mafft (not run; takes too long)
# 6. Run snp-sites
snp-sites
java -jar -Xmx1g TCS.jar
  • 3:10-3:20: interactive visualization with BuTCS
    • Load graph file
    • Load group file
    • Load haplotype file
  • 3:20-3:30: Q & A